Overview

Dataset statistics

Number of variables16
Number of observations1000
Missing cells1270
Missing cells (%)7.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory527.2 KiB
Average record size in memory539.9 B

Variable types

DateTime1
Categorical7
Numeric8

Alerts

source has constant value "Offline"Constant
adstock_alpha is highly overall correlated with c0 and 4 other fieldsHigh correlation
c0 is highly overall correlated with adstock_alpha and 4 other fieldsHigh correlation
c1 is highly overall correlated with adstock_alpha and 4 other fieldsHigh correlation
c2 is highly overall correlated with adstock_alpha and 4 other fieldsHigh correlation
c3 is highly overall correlated with adstock_alpha and 4 other fieldsHigh correlation
carryover_contribution is highly overall correlated with imme_contribution and 2 other fieldsHigh correlation
factors is highly overall correlated with adstock_alpha and 4 other fieldsHigh correlation
imme_contribution is highly overall correlated with carryover_contribution and 2 other fieldsHigh correlation
spend is highly overall correlated with carryover_contribution and 2 other fieldsHigh correlation
total_contribution is highly overall correlated with carryover_contribution and 2 other fieldsHigh correlation
spend has 45 (4.5%) missing valuesMissing
adstock_alpha has 245 (24.5%) missing valuesMissing
imme_contribution has 245 (24.5%) missing valuesMissing
carryover_contribution has 245 (24.5%) missing valuesMissing
saturation_lam has 245 (24.5%) missing valuesMissing
saturation_beta has 245 (24.5%) missing valuesMissing
total_contribution has 167 (16.7%) zerosZeros
spend has 252 (25.2%) zerosZeros
imme_contribution has 150 (15.0%) zerosZeros
carryover_contribution has 150 (15.0%) zerosZeros

Reproduction

Analysis started2026-01-27 03:55:59.388506
Analysis finished2026-01-27 03:56:07.464687
Duration8.08 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

date
Date

Distinct52
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size15.6 KiB
Minimum2021-01-01 00:00:00
Maximum2025-04-01 00:00:00
2026-01-27T11:56:07.537025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:07.686582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

factors
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size83.0 KiB
atl_ip_spend
 
30
ec_tmall_livestream_spend
 
26
atl_dy_kos_spend
 
25
atl_dy_kos_feeds_spend
 
25
atl_ott_spend
 
25
Other values (44)
869 

Length

Max length33
Median length28
Mean length20.039
Min length9

Characters and Unicode

Total characters20039
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowatl_dy_kol_feeds_spend
2nd rowatl_dy_kos_feeds_spend
3rd rowctrl_top5_market_share_std
4th rowatl_dy_kos_spend
5th rowec_dy_expert_live_spend

Common Values

ValueCountFrequency (%)
atl_ip_spend 30
 
3.0%
ec_tmall_livestream_spend 26
 
2.6%
atl_dy_kos_spend 25
 
2.5%
atl_dy_kos_feeds_spend 25
 
2.5%
atl_ott_spend 25
 
2.5%
ec_dy_expert_live_spend 24
 
2.4%
ddt_exhibition_spend 24
 
2.4%
ctrl_own_brand_npd_std 24
 
2.4%
intercept 24
 
2.4%
tmkt_pg_st_spend 23
 
2.3%
Other values (39) 750
75.0%

Length

2026-01-27T11:56:07.827032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl_ip_spend 30
 
3.0%
ec_tmall_livestream_spend 26
 
2.6%
atl_dy_kos_spend 25
 
2.5%
atl_dy_kos_feeds_spend 25
 
2.5%
atl_ott_spend 25
 
2.5%
ec_dy_expert_live_spend 24
 
2.4%
ddt_exhibition_spend 24
 
2.4%
ctrl_own_brand_npd_std 24
 
2.4%
intercept 24
 
2.4%
ec_tmall_cps_spend 23
 
2.3%
Other values (39) 750
75.0%

Most occurring characters

ValueCountFrequency (%)
_ 3001
15.0%
t 1966
9.8%
d 1795
9.0%
e 1784
8.9%
s 1657
 
8.3%
p 1321
 
6.6%
n 1309
 
6.5%
l 1057
 
5.3%
a 975
 
4.9%
o 842
 
4.2%
Other values (18) 4332
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20039
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 3001
15.0%
t 1966
9.8%
d 1795
9.0%
e 1784
8.9%
s 1657
 
8.3%
p 1321
 
6.6%
n 1309
 
6.5%
l 1057
 
5.3%
a 975
 
4.9%
o 842
 
4.2%
Other values (18) 4332
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20039
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 3001
15.0%
t 1966
9.8%
d 1795
9.0%
e 1784
8.9%
s 1657
 
8.3%
p 1321
 
6.6%
n 1309
 
6.5%
l 1057
 
5.3%
a 975
 
4.9%
o 842
 
4.2%
Other values (18) 4332
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20039
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 3001
15.0%
t 1966
9.8%
d 1795
9.0%
e 1784
8.9%
s 1657
 
8.3%
p 1321
 
6.6%
n 1309
 
6.5%
l 1057
 
5.3%
a 975
 
4.9%
o 842
 
4.2%
Other values (18) 4332
21.6%

total_contribution
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct805
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1291978.3
Minimum-6880378.7
Maximum41734464
Zeros167
Zeros (%)16.7%
Negative50
Negative (%)5.0%
Memory size15.6 KiB
2026-01-27T11:56:07.957586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6880378.7
5-th percentile-1271.0002
Q14231.3844
median121367.68
Q3408496.07
95-th percentile3506398
Maximum41734464
Range48614842
Interquartile range (IQR)404264.69

Descriptive statistics

Standard deviation6166852.8
Coefficient of variation (CV)4.773186
Kurtosis35.041757
Mean1291978.3
Median Absolute Deviation (MAD)121367.68
Skewness5.9886654
Sum1.2919783 × 109
Variance3.8030073 × 1013
MonotonicityNot monotonic
2026-01-27T11:56:08.092136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 167
 
16.7%
220406.3033 9
 
0.9%
-258506.7076 4
 
0.4%
-3817697.712 3
 
0.3%
-988279.1761 3
 
0.3%
3506397.999 3
 
0.3%
-1016875.217 2
 
0.2%
-2264068.03 2
 
0.2%
1047439.291 2
 
0.2%
1088442.252 2
 
0.2%
Other values (795) 803
80.3%
ValueCountFrequency (%)
-6880378.72 2
0.2%
-3817697.712 3
0.3%
-3103940.543 1
 
0.1%
-2980744.464 1
 
0.1%
-2438619.815 1
 
0.1%
-2311851.83 2
0.2%
-2264068.03 2
0.2%
-2084918.077 1
 
0.1%
-2043232.269 1
 
0.1%
-1860163.728 1
 
0.1%
ValueCountFrequency (%)
41734463.53 1
0.1%
41728475.09 1
0.1%
41601776.73 1
0.1%
41454682.92 1
0.1%
41402253.32 1
0.1%
41379899.64 1
0.1%
41178298.33 1
0.1%
41072843.54 1
0.1%
41039024.48 1
0.1%
40984459.86 1
0.1%

spend
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct671
Distinct (%)70.3%
Missing45
Missing (%)4.5%
Infinite0
Infinite (%)0.0%
Mean359264.06
Minimum0
Maximum4324718
Zeros252
Zeros (%)25.2%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:08.226941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53340.897
Q3453858.91
95-th percentile1674901
Maximum4324718
Range4324718
Interquartile range (IQR)453858.91

Descriptive statistics

Standard deviation605209.91
Coefficient of variation (CV)1.6845824
Kurtosis6.9498735
Mean359264.06
Median Absolute Deviation (MAD)53340.897
Skewness2.4341402
Sum3.4309717 × 108
Variance3.6627903 × 1011
MonotonicityNot monotonic
2026-01-27T11:56:08.361571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 252
 
25.2%
0.7986954913 9
 
0.9%
1 7
 
0.7%
0.1094601802 4
 
0.4%
0.6803149606 3
 
0.3%
785700 3
 
0.3%
94340 2
 
0.2%
1504200 2
 
0.2%
1067150 2
 
0.2%
0.7 2
 
0.2%
Other values (661) 669
66.9%
(Missing) 45
 
4.5%
ValueCountFrequency (%)
0 252
25.2%
0.01076365943 1
 
0.1%
0.1015533991 1
 
0.1%
0.1094601802 4
 
0.4%
0.1160676895 1
 
0.1%
0.1340448826 1
 
0.1%
0.1396153015 1
 
0.1%
0.1421610411 1
 
0.1%
0.1980116706 1
 
0.1%
0.2926734385 1
 
0.1%
ValueCountFrequency (%)
4324718 1
0.1%
3882306 1
0.1%
3436807 1
0.1%
3225204.705 1
0.1%
3064597.434 1
0.1%
2963543.301 1
0.1%
2919383.468 1
0.1%
2823394.988 1
0.1%
2752941.88 1
0.1%
2752238 1
0.1%

year
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size67.4 KiB
2022
237 
2021
231 
2024
230 
2023
222 
2025
80 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2024
3rd row2021
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2022 237
23.7%
2021 231
23.1%
2024 230
23.0%
2023 222
22.2%
2025 80
 
8.0%

Length

2026-01-27T11:56:08.482220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T11:56:08.584394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 237
23.7%
2021 231
23.1%
2024 230
23.0%
2023 222
22.2%
2025 80
 
8.0%

Most occurring characters

ValueCountFrequency (%)
2 2237
55.9%
0 1000
25.0%
1 231
 
5.8%
4 230
 
5.8%
3 222
 
5.5%
5 80
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 2237
55.9%
0 1000
25.0%
1 231
 
5.8%
4 230
 
5.8%
3 222
 
5.5%
5 80
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 2237
55.9%
0 1000
25.0%
1 231
 
5.8%
4 230
 
5.8%
3 222
 
5.5%
5 80
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 2237
55.9%
0 1000
25.0%
1 231
 
5.8%
4 230
 
5.8%
3 222
 
5.5%
5 80
 
2.0%

month
Real number (ℝ)

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.247
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:08.694411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4371216
Coefficient of variation (CV)0.55020355
Kurtosis-1.1999291
Mean6.247
Median Absolute Deviation (MAD)3
Skewness0.10213124
Sum6247
Variance11.813805
MonotonicityNot monotonic
2026-01-27T11:56:08.803202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 96
9.6%
4 95
9.5%
3 91
9.1%
7 88
8.8%
1 87
8.7%
6 84
8.4%
8 81
8.1%
9 79
7.9%
5 76
7.6%
12 76
7.6%
Other values (2) 147
14.7%
ValueCountFrequency (%)
1 87
8.7%
2 96
9.6%
3 91
9.1%
4 95
9.5%
5 76
7.6%
6 84
8.4%
7 88
8.8%
8 81
8.1%
9 79
7.9%
10 73
7.3%
ValueCountFrequency (%)
12 76
7.6%
11 74
7.4%
10 73
7.3%
9 79
7.9%
8 81
8.1%
7 88
8.8%
6 84
8.4%
5 76
7.6%
4 95
9.5%
3 91
9.1%

source
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.3 KiB
Offline
1000 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters7000
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOffline
3rd rowOffline
4th rowOffline
5th rowOffline

Common Values

ValueCountFrequency (%)
Offline 1000
100.0%

Length

2026-01-27T11:56:08.918382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T11:56:09.129535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
offline 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
f 2000
28.6%
O 1000
14.3%
l 1000
14.3%
i 1000
14.3%
n 1000
14.3%
e 1000
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 2000
28.6%
O 1000
14.3%
l 1000
14.3%
i 1000
14.3%
n 1000
14.3%
e 1000
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 2000
28.6%
O 1000
14.3%
l 1000
14.3%
i 1000
14.3%
n 1000
14.3%
e 1000
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 2000
28.6%
O 1000
14.3%
l 1000
14.3%
i 1000
14.3%
n 1000
14.3%
e 1000
14.3%

c0
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size71.6 KiB
Offline
737 
base_Offline
263 

Length

Max length12
Median length7
Mean length8.315
Min length7

Characters and Unicode

Total characters8315
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOffline
2nd rowOffline
3rd rowbase_Offline
4th rowOffline
5th rowOffline

Common Values

ValueCountFrequency (%)
Offline 737
73.7%
base_Offline 263
 
26.3%

Length

2026-01-27T11:56:09.228721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T11:56:09.321914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
offline 737
73.7%
base_offline 263
 
26.3%

Most occurring characters

ValueCountFrequency (%)
f 2000
24.1%
e 1263
15.2%
O 1000
12.0%
l 1000
12.0%
i 1000
12.0%
n 1000
12.0%
b 263
 
3.2%
a 263
 
3.2%
s 263
 
3.2%
_ 263
 
3.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
f 2000
24.1%
e 1263
15.2%
O 1000
12.0%
l 1000
12.0%
i 1000
12.0%
n 1000
12.0%
b 263
 
3.2%
a 263
 
3.2%
s 263
 
3.2%
_ 263
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
f 2000
24.1%
e 1263
15.2%
O 1000
12.0%
l 1000
12.0%
i 1000
12.0%
n 1000
12.0%
b 263
 
3.2%
a 263
 
3.2%
s 263
 
3.2%
_ 263
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8315
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
f 2000
24.1%
e 1263
15.2%
O 1000
12.0%
l 1000
12.0%
i 1000
12.0%
n 1000
12.0%
b 263
 
3.2%
a 263
 
3.2%
s 263
 
3.2%
_ 263
 
3.2%

c1
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size66.5 KiB
ATL
279 
Base
263 
EC
222 
DDT
141 
TMKT
95 

Length

Max length4
Median length3
Mean length3.136
Min length2

Characters and Unicode

Total characters3136
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATL
2nd rowATL
3rd rowBase
4th rowATL
5th rowEC

Common Values

ValueCountFrequency (%)
ATL 279
27.9%
Base 263
26.3%
EC 222
22.2%
DDT 141
14.1%
TMKT 95
 
9.5%

Length

2026-01-27T11:56:09.430551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T11:56:09.542248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
atl 279
27.9%
base 263
26.3%
ec 222
22.2%
ddt 141
14.1%
tmkt 95
 
9.5%

Most occurring characters

ValueCountFrequency (%)
T 610
19.5%
D 282
9.0%
A 279
8.9%
L 279
8.9%
B 263
8.4%
a 263
8.4%
s 263
8.4%
e 263
8.4%
E 222
 
7.1%
C 222
 
7.1%
Other values (2) 190
 
6.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 610
19.5%
D 282
9.0%
A 279
8.9%
L 279
8.9%
B 263
8.4%
a 263
8.4%
s 263
8.4%
e 263
8.4%
E 222
 
7.1%
C 222
 
7.1%
Other values (2) 190
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 610
19.5%
D 282
9.0%
A 279
8.9%
L 279
8.9%
B 263
8.4%
a 263
8.4%
s 263
8.4%
e 263
8.4%
E 222
 
7.1%
C 222
 
7.1%
Other values (2) 190
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 610
19.5%
D 282
9.0%
A 279
8.9%
L 279
8.9%
B 263
8.4%
a 263
8.4%
s 263
8.4%
e 263
8.4%
E 222
 
7.1%
C 222
 
7.1%
Other values (2) 190
 
6.1%

c2
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size67.6 KiB
ATL
279 
EC
222 
control
218 
DDT
141 
TMKT
95 
Other values (2)
45 

Length

Max length18
Median length9
Mean length4.204
Min length2

Characters and Unicode

Total characters4204
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATL
2nd rowATL
3rd rowcontrol
4th rowATL
5th rowEC

Common Values

ValueCountFrequency (%)
ATL 279
27.9%
EC 222
22.2%
control 218
21.8%
DDT 141
14.1%
TMKT 95
 
9.5%
intercept 24
 
2.4%
yearly_seasonality 21
 
2.1%

Length

2026-01-27T11:56:09.689293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-27T11:56:09.810515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
atl 279
27.9%
ec 222
22.2%
control 218
21.8%
ddt 141
14.1%
tmkt 95
 
9.5%
intercept 24
 
2.4%
yearly_seasonality 21
 
2.1%

Most occurring characters

ValueCountFrequency (%)
T 610
14.5%
o 457
10.9%
t 287
 
6.8%
D 282
 
6.7%
A 279
 
6.6%
L 279
 
6.6%
r 263
 
6.3%
n 263
 
6.3%
l 260
 
6.2%
c 242
 
5.8%
Other values (11) 982
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4204
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 610
14.5%
o 457
10.9%
t 287
 
6.8%
D 282
 
6.7%
A 279
 
6.6%
L 279
 
6.6%
r 263
 
6.3%
n 263
 
6.3%
l 260
 
6.2%
c 242
 
5.8%
Other values (11) 982
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4204
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 610
14.5%
o 457
10.9%
t 287
 
6.8%
D 282
 
6.7%
A 279
 
6.6%
L 279
 
6.6%
r 263
 
6.3%
n 263
 
6.3%
l 260
 
6.2%
c 242
 
5.8%
Other values (11) 982
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4204
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 610
14.5%
o 457
10.9%
t 287
 
6.8%
D 282
 
6.7%
A 279
 
6.6%
L 279
 
6.6%
r 263
 
6.3%
n 263
 
6.3%
l 260
 
6.2%
c 242
 
5.8%
Other values (11) 982
23.4%

c3
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size71.5 KiB
ATL
279 
EC
222 
DDT
141 
TMKT
95 
intercept
 
24
Other values (12)
239 

Length

Max length32
Median length31
Mean length8.174
Min length2

Characters and Unicode

Total characters8174
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATL
2nd rowATL
3rd rowctrl_top5_market_share_std
4th rowATL
5th rowEC

Common Values

ValueCountFrequency (%)
ATL 279
27.9%
EC 222
22.2%
DDT 141
14.1%
TMKT 95
 
9.5%
intercept 24
 
2.4%
ctrl_own_brand_npd_std 24
 
2.4%
ctrl_macro_trscg_p_std 23
 
2.3%
ctrl_competitor_asp_std 21
 
2.1%
ctrl_competitor_total_sales_std 21
 
2.1%
ctrl_own_brand_discount_rate_std 21
 
2.1%
Other values (7) 129
12.9%

Length

2026-01-27T11:56:09.948056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
atl 279
27.9%
ec 222
22.2%
ddt 141
14.1%
tmkt 95
 
9.5%
intercept 24
 
2.4%
ctrl_own_brand_npd_std 24
 
2.4%
ctrl_macro_trscg_p_std 23
 
2.3%
ctrl_own_brand_discount_rate_std 21
 
2.1%
yearly_seasonality 21
 
2.1%
ctrl_competitor_total_sales_std 21
 
2.1%
Other values (7) 129
12.9%

Most occurring characters

ValueCountFrequency (%)
_ 881
 
10.8%
t 804
 
9.8%
T 610
 
7.5%
r 527
 
6.4%
c 415
 
5.1%
s 405
 
5.0%
d 400
 
4.9%
o 395
 
4.8%
n 346
 
4.2%
l 340
 
4.2%
Other values (22) 3051
37.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
_ 881
 
10.8%
t 804
 
9.8%
T 610
 
7.5%
r 527
 
6.4%
c 415
 
5.1%
s 405
 
5.0%
d 400
 
4.9%
o 395
 
4.8%
n 346
 
4.2%
l 340
 
4.2%
Other values (22) 3051
37.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
_ 881
 
10.8%
t 804
 
9.8%
T 610
 
7.5%
r 527
 
6.4%
c 415
 
5.1%
s 405
 
5.0%
d 400
 
4.9%
o 395
 
4.8%
n 346
 
4.2%
l 340
 
4.2%
Other values (22) 3051
37.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
_ 881
 
10.8%
t 804
 
9.8%
T 610
 
7.5%
r 527
 
6.4%
c 415
 
5.1%
s 405
 
5.0%
d 400
 
4.9%
o 395
 
4.8%
n 346
 
4.2%
l 340
 
4.2%
Other values (22) 3051
37.3%

adstock_alpha
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)4.9%
Missing245
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean0.34833728
Minimum0.15208842
Maximum0.5396742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:10.070858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15208842
5-th percentile0.19324492
Q10.25065067
median0.36675043
Q30.44450253
95-th percentile0.49995468
Maximum0.5396742
Range0.38758578
Interquartile range (IQR)0.19385186

Descriptive statistics

Standard deviation0.10223268
Coefficient of variation (CV)0.29348763
Kurtosis-1.1388407
Mean0.34833728
Median Absolute Deviation (MAD)0.10222627
Skewness-0.03100824
Sum262.99465
Variance0.010451522
MonotonicityNot monotonic
2026-01-27T11:56:10.211644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.4537456717 30
 
3.0%
0.2015771648 26
 
2.6%
0.4745182302 25
 
2.5%
0.3872391098 25
 
2.5%
0.3915610042 25
 
2.5%
0.1520884173 24
 
2.4%
0.4445617273 24
 
2.4%
0.246045076 23
 
2.3%
0.3360199254 23
 
2.3%
0.2506506721 23
 
2.3%
Other values (27) 507
50.7%
(Missing) 245
24.5%
ValueCountFrequency (%)
0.1520884173 24
2.4%
0.1932449154 23
2.3%
0.2015771648 26
2.6%
0.246045076 23
2.3%
0.2483098525 19
1.9%
0.2485899787 19
1.9%
0.2489862168 15
1.5%
0.2503846768 20
2.0%
0.2506397608 15
1.5%
0.2506506721 23
2.3%
ValueCountFrequency (%)
0.5396741956 16
1.6%
0.5000080459 20
2.0%
0.4999546848 17
1.7%
0.4982660608 21
2.1%
0.4745182302 25
2.5%
0.4689766964 16
1.6%
0.4627234416 18
1.8%
0.4537456717 30
3.0%
0.4445617273 24
2.4%
0.4445025317 16
1.6%

imme_contribution
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct606
Distinct (%)80.3%
Missing245
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean117925.07
Minimum0
Maximum924824.17
Zeros150
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:10.352103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14005.9864
median60861.875
Q3140956.06
95-th percentile455871.6
Maximum924824.17
Range924824.17
Interquartile range (IQR)136950.07

Descriptive statistics

Standard deviation169960.58
Coefficient of variation (CV)1.4412591
Kurtosis6.6604671
Mean117925.07
Median Absolute Deviation (MAD)60216.332
Skewness2.435648
Sum89033428
Variance2.88866 × 1010
MonotonicityNot monotonic
2026-01-27T11:56:10.486653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150
 
15.0%
22943.90215 1
 
0.1%
112756.8344 1
 
0.1%
540833.6415 1
 
0.1%
129603.7612 1
 
0.1%
53317.15336 1
 
0.1%
189017.3131 1
 
0.1%
74179.66534 1
 
0.1%
80298.0076 1
 
0.1%
71752.72372 1
 
0.1%
Other values (596) 596
59.6%
(Missing) 245
24.5%
ValueCountFrequency (%)
0 150
15.0%
25.79190899 1
 
0.1%
57.18438607 1
 
0.1%
86.63099075 1
 
0.1%
141.2170008 1
 
0.1%
176.2275872 1
 
0.1%
185.7806249 1
 
0.1%
210.2691828 1
 
0.1%
271.8212256 1
 
0.1%
337.8302293 1
 
0.1%
ValueCountFrequency (%)
924824.1718 1
0.1%
916056.215 1
0.1%
915527.0636 1
0.1%
913569.1524 1
0.1%
898570.5718 1
0.1%
884261.0841 1
0.1%
875323.5006 1
0.1%
871766.4363 1
0.1%
865874.6424 1
0.1%
847648.4668 1
0.1%

carryover_contribution
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct606
Distinct (%)80.3%
Missing245
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean74889.637
Minimum0
Maximum1084240.2
Zeros150
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:10.626379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11654.5677
median25413.468
Q374356.498
95-th percentile288096.47
Maximum1084240.2
Range1084240.2
Interquartile range (IQR)72701.93

Descriptive statistics

Standard deviation149080.22
Coefficient of variation (CV)1.9906656
Kurtosis23.442083
Mean74889.637
Median Absolute Deviation (MAD)25413.468
Skewness4.416611
Sum56541676
Variance2.2224913 × 1010
MonotonicityNot monotonic
2026-01-27T11:56:10.772621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 150
 
15.0%
14499.58113 1
 
0.1%
37303.49378 1
 
0.1%
273699.2974 1
 
0.1%
42876.98502 1
 
0.1%
13460.93841 1
 
0.1%
120450.6669 1
 
0.1%
43577.24404 1
 
0.1%
41023.56776 1
 
0.1%
59601.3361 1
 
0.1%
Other values (596) 596
59.6%
(Missing) 245
24.5%
ValueCountFrequency (%)
0 150
15.0%
6.178027714 1
 
0.1%
24.96026014 1
 
0.1%
37.81333008 1
 
0.1%
46.64897199 1
 
0.1%
58.21420744 1
 
0.1%
69.45935097 1
 
0.1%
112.9946366 1
 
0.1%
142.0337012 1
 
0.1%
154.3185108 1
 
0.1%
ValueCountFrequency (%)
1084240.197 1
0.1%
1073960.869 1
0.1%
1073340.506 1
0.1%
1071045.101 1
0.1%
1053461.149 1
0.1%
1036685.071 1
0.1%
1022036.666 1
0.1%
1015129.277 1
0.1%
987538.8271 1
0.1%
905705.3036 1
0.1%

saturation_lam
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)4.9%
Missing245
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean2.0762712
Minimum0.33421412
Maximum6.0008514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:10.893630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.33421412
5-th percentile0.66521806
Q11.3591955
median1.8323941
Q32.3645662
95-th percentile4.0138641
Maximum6.0008514
Range5.6666372
Interquartile range (IQR)1.0053707

Descriptive statistics

Standard deviation1.034103
Coefficient of variation (CV)0.49805777
Kurtosis3.2122727
Mean2.0762712
Median Absolute Deviation (MAD)0.498372
Skewness1.5812987
Sum1567.5847
Variance1.069369
MonotonicityNot monotonic
2026-01-27T11:56:11.017309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
2.377180623 30
 
3.0%
3.983940273 26
 
2.6%
1.832394135 25
 
2.5%
1.959947802 25
 
2.5%
1.579286762 25
 
2.5%
2.633878769 24
 
2.4%
1.288084619 24
 
2.4%
2.333510783 23
 
2.3%
1.598818577 23
 
2.3%
4.01386408 23
 
2.3%
Other values (27) 507
50.7%
(Missing) 245
24.5%
ValueCountFrequency (%)
0.3342141249 20
2.0%
0.6652180642 19
1.9%
0.9844153854 15
1.5%
1.288084619 24
2.4%
1.323569041 15
1.5%
1.324214624 18
1.8%
1.326657245 21
2.1%
1.32713139 21
2.1%
1.334022131 20
2.0%
1.359195489 22
2.2%
ValueCountFrequency (%)
6.000851374 15
1.5%
4.698029348 15
1.5%
4.01386408 23
2.3%
3.983940273 26
2.6%
3.334208432 19
1.9%
2.665447683 20
2.0%
2.633878769 24
2.4%
2.377180623 30
3.0%
2.364566237 23
2.3%
2.333510783 23
2.3%

saturation_beta
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)4.9%
Missing245
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean0.011754277
Minimum0.0015232275
Maximum0.044895893
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.6 KiB
2026-01-27T11:56:11.142590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015232275
5-th percentile0.0017965323
Q10.0047264541
median0.0083888894
Q30.0140037
95-th percentile0.035437178
Maximum0.044895893
Range0.043372666
Interquartile range (IQR)0.0092772463

Descriptive statistics

Standard deviation0.010048915
Coefficient of variation (CV)0.8549156
Kurtosis2.363903
Mean0.011754277
Median Absolute Deviation (MAD)0.0044960704
Skewness1.6897087
Sum8.8744794
Variance0.00010098069
MonotonicityNot monotonic
2026-01-27T11:56:11.267237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.01875606206 30
 
3.0%
0.003892819044 26
 
2.6%
0.02427601767 25
 
2.5%
0.00518681978 25
 
2.5%
0.009442132485 25
 
2.5%
0.004126222597 24
 
2.4%
0.006371321615 24
 
2.4%
0.008388889425 23
 
2.3%
0.03543717775 23
 
2.3%
0.003223064762 23
 
2.3%
Other values (27) 507
50.7%
(Missing) 245
24.5%
ValueCountFrequency (%)
0.001523227534 15
1.5%
0.001715687929 15
1.5%
0.001796532264 20
2.0%
0.003223064762 23
2.3%
0.003244253067 16
1.6%
0.003460939638 19
1.9%
0.003892819044 26
2.6%
0.004126222597 24
2.4%
0.004571608604 20
2.0%
0.004726454074 19
1.9%
ValueCountFrequency (%)
0.04489589319 16
1.6%
0.03921022512 21
2.1%
0.03543717775 23
2.3%
0.02427601767 25
2.5%
0.02416057794 23
2.3%
0.0203305953 15
1.5%
0.01875606206 30
3.0%
0.01566989691 20
2.0%
0.01400370033 22
2.2%
0.01355461115 16
1.6%

Interactions

2026-01-27T11:56:06.077638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.040658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.909042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.743721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.580569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.476634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.326386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.262774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.190330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.149729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.004707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.851896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.702055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.590630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.444646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.376335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.318823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.251320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.099391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.954479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.812390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.699399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.538881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.473003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.428226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.352234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.203221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.053810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.931412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.796034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.635649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.569846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.542196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.475762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.320830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.182525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.053925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.908652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.876442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.684036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.650080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.579618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.423487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.283196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.155213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.004404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.973638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.774733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.763422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.697122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.529390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.389841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.266476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.116953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.072308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.879303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:06.862082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:00.803774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:01.636643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:02.489506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:03.370072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:04.225720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.166629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2026-01-27T11:56:05.982955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2026-01-27T11:56:11.361850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
adstock_alphac0c1c2c3carryover_contributionfactorsimme_contributionmonthsaturation_betasaturation_lamspendtotal_contributionyear
adstock_alpha1.0000.6700.6790.6790.6790.1690.982-0.017-0.0120.329-0.329-0.1210.0550.000
c00.6701.0000.9980.9970.992-0.0680.976-0.049-0.0310.086-0.198-0.2940.2620.000
c10.6790.9981.0000.9990.994-0.0350.9780.060-0.003-0.3700.2400.211-0.0580.000
c20.6790.9970.9991.0000.995-0.0530.9790.045-0.030-0.3210.172-0.0980.2250.000
c30.6790.9920.9940.9951.000-0.0530.9840.045-0.029-0.3210.172-0.0980.2350.000
carryover_contribution0.169-0.068-0.035-0.053-0.0531.0000.4470.9680.0840.4880.0900.8260.9870.056
factors0.9820.9760.9780.9790.9840.4471.0000.138-0.009-0.3800.2730.2480.0570.000
imme_contribution-0.017-0.0490.0600.0450.0450.9680.1381.0000.0940.4300.1740.8780.9950.101
month-0.012-0.031-0.003-0.030-0.0290.084-0.0090.0941.0000.0050.0370.0560.0260.180
saturation_beta0.3290.086-0.370-0.321-0.3210.488-0.3800.4300.0051.000-0.2100.3060.4570.000
saturation_lam-0.329-0.1980.2400.1720.1720.0900.2730.1740.037-0.2101.0000.2100.1420.000
spend-0.121-0.2940.211-0.098-0.0980.8260.2480.8780.0560.3060.2101.0000.5870.038
total_contribution0.0550.262-0.0580.2250.2350.9870.0570.9950.0260.4570.1420.5871.0000.000
year0.0000.0000.0000.0000.0000.0560.0000.1010.1800.0000.0000.0380.0001.000

Missing values

2026-01-27T11:56:07.003257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-27T11:56:07.222885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2026-01-27T11:56:07.379568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datefactorstotal_contributionspendyearmonthsourcec0c1c2c3adstock_alphaimme_contributioncarryover_contributionsaturation_lamsaturation_beta
562021-05-01atl_dy_kol_feeds_spend0.000000e+000.00000020215OfflineOfflineATLATLATL0.4627230.0000000.0000001.9421700.008401
1942024-03-01atl_dy_kos_feeds_spend2.565592e+040.00000020243OfflineOfflineATLATLATL0.38723915720.9435009934.9750661.9599480.005187
21852021-02-01ctrl_top5_market_share_std2.029070e+061.00000020212Offlinebase_OfflineBasecontrolctrl_top5_market_share_stdNaNNaNNaNNaNNaN
2332023-02-01atl_dy_kos_spend0.000000e+000.00000020232OfflineOfflineATLATLATL0.3915610.0000000.0000001.5792870.009442
13762023-01-01ec_dy_expert_live_spend1.873540e+04103100.76564220231OfflineOfflineECECEC0.15208815885.9624412849.4372922.6338790.004126
18472023-04-01ec_tmart_cps_spend2.762289e+0490299.19453420234OfflineOfflineECECEC0.24898620745.1723756877.7192946.0008510.001523
15672021-08-01ec_jd_onsite_spend1.982995e+05757825.66000020218OfflineOfflineECECEC0.246045149508.88609448790.6160762.3335110.008389
7872021-08-01tmkt_pg_st_spend6.822831e+05693750.00000020218OfflineOfflineTMKTTMKTTMKT0.336020453022.382655229260.7158751.5988190.035437
23042022-05-01ctrl_competitor_total_sales_std4.454926e+060.86201320225Offlinebase_OfflineBasecontrolctrl_competitor_total_sales_stdNaNNaNNaNNaNNaN
1732022-06-01atl_dy_kos_feeds_spend0.000000e+000.00000020226OfflineOfflineATLATLATL0.3872390.0000000.0000001.9599480.005187
datefactorstotal_contributionspendyearmonthsourcec0c1c2c3adstock_alphaimme_contributioncarryover_contributionsaturation_lamsaturation_beta
20162024-05-01ctrl_macro_trscg_p_std2.383578e+050.86374720245Offlinebase_OfflineBasecontrolctrl_macro_trscg_p_stdNaNNaNNaNNaNNaN
20042023-05-01ctrl_macro_trscg_p_std2.298006e+050.83273820235Offlinebase_OfflineBasecontrolctrl_macro_trscg_p_stdNaNNaNNaNNaNNaN
22112023-04-01ctrl_top5_market_share_std1.776970e+060.87575620234Offlinebase_OfflineBasecontrolctrl_top5_market_share_stdNaNNaNNaNNaNNaN
19532023-06-01ctrl_macro_cci_std-9.882792e+050.68031520236Offlinebase_OfflineBasecontrolctrl_macro_cci_stdNaNNaNNaNNaNNaN
12102022-03-01ddt_hybrid_spend0.000000e+000.00000020223OfflineOfflineDDTDDTDDT0.3754680.0000000.0000001.3235690.001716
21472022-04-01ctrl_own_brand_offline_wd_std1.054894e+060.85241020224Offlinebase_OfflineBasecontrolctrl_own_brand_offline_wd_stdNaNNaNNaNNaNNaN
1912023-12-01atl_dy_kos_feeds_spend1.531897e+030.000000202312OfflineOfflineATLATLATL0.387239938.686528593.2104041.9599480.005187
13502025-03-01ddt_sampling_spend2.031509e+05973169.60000020253OfflineOfflineDDTDDTDDT0.338139134457.62458768693.2545041.6354130.013428
11242023-09-01ddt_exhibition_spend0.000000e+000.00000020239OfflineOfflineDDTDDTDDT0.4445620.0000000.0000001.2880850.006371
25042021-09-01intercept4.160178e+07NaN20219Offlinebase_OfflineBaseinterceptinterceptNaNNaNNaNNaNNaN